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Curricular information is subject to change
On successful completion of this module the learner will be able to:
- Understand the typical recommender system architecture and recommendation tasks.
- Understand core algorithms driving common recommender systems including the pros and cons of each.
- Learn about different approaches to evaluating recommender systems, using a variety of metrics and methodologies.
- Learn about more contemporary recommender systems research covering a variety of more advanced topics.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Practical | 10 |
Autonomous Student Learning | 80 |
Total | 114 |
Proficiency in the Java Programming Language is required. There is a significant software engineering effort required and so students must be comfortable and proficient in developing complex programs using advanced tools and techniques.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: In-class test | Throughout the Trimester | n/a | Alternative linear conversion grade scale 40% | No | 20 |
Continuous Assessment: Practical projects | Throughout the Trimester | n/a | Alternative linear conversion grade scale 40% | No | 40 |
Continuous Assessment: Practical report | Throughout the Trimester | n/a | Graded | No | 40 |
Resit In | Terminal Exam |
---|---|
Summer | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Post-assessment, feedback will be provided to students in class. Individual feedback is also available to students. During practical sessions, a teaching assistant and demonstrators will be available to provide assistance and feedback to students on their work.